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WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation

文献类型：期刊论文

作者Cai, Tingting1,2; Yan, Hongping1; Ding, Kun2; Zhang, Yan1,2; Zhou, Yueyue1,2
刊名APPLIED SCIENCES-BASEL
出版日期2024-06-01
卷号14期号:12页码:16
关键词weakly supervised learning polyp segmentation Segment Anything Model pseudo-label generation deep learning
DOI10.3390/app14125007
通讯作者Yan, Hongping(yanhp@cugb.edu.cn)
英文摘要Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully supervised, requiring extensive, precise, manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model-the Segment Anything Model (SAM)-into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPolyp-SAM, a novel weakly supervised approach for colonoscopy polyp segmentation. WSPolyp-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotation data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPolyp-SAM outperforms current fully supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly supervised and fully supervised experiments, it is found that weakly supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully supervised fine-tuning. This study provides a new perspective on the combination of weakly supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.
WOS关键词NETWORK
资助项目National Natural Science Foundation of China[62306310]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:001254620300001
出版者MDPI
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/59148]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Yan, Hongping
作者单位1.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Tingting,Yan, Hongping,Ding, Kun,et al. WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation[J]. APPLIED SCIENCES-BASEL,2024,14(12):16.
APA Cai, Tingting,Yan, Hongping,Ding, Kun,Zhang, Yan,&Zhou, Yueyue.(2024).WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation.APPLIED SCIENCES-BASEL,14(12),16.
MLA Cai, Tingting,et al."WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation".APPLIED SCIENCES-BASEL 14.12(2024):16.

入库方式： OAI收割

来源：自动化研究所

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